With the rapid development of Internet information technologies, the explosive growth of online learning resources has caused the problem of “information overload” and “learning disorientation”. In the absence of expert guidance, it is difficult for users to identify their learning demands and select the appropriate content from the vast amount of learning resources. Educational domain recommendation methods have received a lot of attention from researchers in recent years because they can provide personalized recommendations of learning resources based on the historical learning behaviors of users. However, the existing educational domain recommendation methods ignore the modeling of complex relationships among knowledge points in learning demand perception and fail to consider the dynamic changes of users’ learning demands, which leads to inaccurate learning resource recommendations. To address the above problems, this study proposes a knowledge point recommendation method based on static and dynamic learning demand perception, which models users’ learning behaviors under complex knowledge association by combining static perception and dynamic perception. For static learning demand perception, this study innovatively designs an attentional graph convolutional network based on the first-course-following meta-path guidance of knowledge points, which can accurately capture users’ static learning demands at the fine-grained knowledge point level by modeling the complex constraints of the first-course-following relationship between knowledge points and eliminating the interference of other non-learning demand factors. For dynamic learning demand perception, the method aggregates knowledge point embeddings to characterize users’ knowledge levels at different moments by taking courses as units and then uses a recurrent neural network to encode users’ knowledge level sequences, which can effectively explore the dynamic learning demands hidden in users’ knowledge level changes. Finally, this study fuses the obtained static and dynamic learning demands, models the compatibility between static and dynamic learning demands in the same framework, and promotes the complementarity of these two learning demands to achieve fine-grained and personalized knowledge point recommendations. Experiments show that the proposed method can effectively perceive users’ learning demands, provide personalized knowledge point recommendations on two publicly available datasets, and outperform the mainstream recommendation methods in terms of various evaluation metrics.